What does my group do?
- Study the molecular basis of variation in development and disease
- Using high-throughput experimental methods
August 3, 2016
What does my group do?
metagenomeSeqmetagenomeFeaturesantiProfilesminfibumphunterHTShapeqsmoothRcplexRcsdpCollaborative and exploratory analysis
Bsmooth, minfi)epivizr packageCreativity in exploration
We are building software applications to support creative exploratory analysis of large genome-wide datasets…
Summarization: summarize integrated measurements (computed on data subsets)
Statistically-guided exploration: Calculate a statistic of interest
# Get tumor methylation base-pair data m <- assay(se)[,"tumor"] # Compute regions with highest variability across cpgs region_stat <- calcWindowStat(m, step=25, window=80, stat=rowSds) s <- region_stat[,"stat"]
Explore data based on statistic
What's around the regions with highest across CpG variability?
# get locations in decreasing order o <- order(s, decreasing=TRUE) indices <- region_stat[o, "indices"] slideShowRegions <- rowRanges(se)[indices] + 1250000L mgr$slideshow(slideShowRegions)
dynamically extensible: Easily integrate new data types and add new visualizations.
Visualization design goals
Visualization goals
One interpretation of Big Data is Many relevant sources of contextual data
metagenomeSeq, metagenomicFeatures, metavizWhat is the measurement?
What is the measurement?
Features:
Hierachically organized features
Hierarchically organized features
Defining the measurement unit of analysis
Not just features, but samples may be hierarchically organized
metagenomeSeq, metagenomeFeatures, metavizr)Acknowledgements
Florin Chelaru (now at Twinfog), Joseph Paulson (now at Harvard)
Justin Wagner, Jayaram Kancherla (CBCB)
Mihai Pop (CBCB)
HMP2 Project
Funding: NIH, Genentech, Gates Foundation
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